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Models for Counts and Particle Size Distributions of Sub-visible Particle Data

Jorge Quiroz, Elsa M. Vazquez, Jeffrey Wilson, Anita Dabbara and Jason Cheung
PDA Journal of Pharmaceutical Science and Technology November 2020, pdajpst.2020.011510; DOI: https://doi.org/10.5731/pdajpst.2020.011510
Jorge Quiroz
1 Merck & Co.;
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  • For correspondence: jorge.quiroz@merck.com
Elsa M. Vazquez
2 Arizona State University;
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  • For correspondence: elsa.vazquez@asu.edu
Jeffrey Wilson
2 Arizona State University;
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  • For correspondence: jeffrey.wilson@asu.edu
Anita Dabbara
1 Merck & Co.;
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Jason Cheung
3 Merck
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  • For correspondence: jason.cheung@merck.com
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Abstract

Traditional statistical analyses of sub-visible particle data are usually based on either descriptive statistics, normal-based methods, or standard Poisson models. These methods often do not adequately describe the counts or particle size distribution, or ignore relevant information represented in the data, such as count correlation. Therefore, any meaningful analyses of sub-visible particle data require a reasonable representation of counts and the particle size distribution as well as a means of addressing the correlation in the data. Such comprehensive approaches are not widely available or known when analyzing sub-visible particle data. In this paper, we propose the use of generalized linear mixed models to analyze counts and the particle size distribution of sub-visible particle data. These models make optimal use of the information in the data, and allow flexible approaches for the analyses of a wide range of data structures. They are readily accessible to practitioners through the use of modern statistical software. These models are demonstrated with two numerical examples using two different data structures.

  • Generalized linear mixed models
  • Ordinal logistic regression with normal random effects
  • Overdispersion
  • Poisson regression with normal random effects
  • Received March 11, 2020.
  • Accepted October 23, 2020.
  • Copyright © 2020, Parenteral Drug Association

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PDA Journal of Pharmaceutical Science and Technology: 74 (6)
PDA Journal of Pharmaceutical Science and Technology
Vol. 74, Issue 6
November/December 2020
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Models for Counts and Particle Size Distributions of Sub-visible Particle Data
Jorge Quiroz, Elsa M. Vazquez, Jeffrey Wilson, Anita Dabbara, Jason Cheung
PDA Journal of Pharmaceutical Science and Technology Nov 2020, pdajpst.2020.011510; DOI: 10.5731/pdajpst.2020.011510

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Models for Counts and Particle Size Distributions of Sub-visible Particle Data
Jorge Quiroz, Elsa M. Vazquez, Jeffrey Wilson, Anita Dabbara, Jason Cheung
PDA Journal of Pharmaceutical Science and Technology Nov 2020, pdajpst.2020.011510; DOI: 10.5731/pdajpst.2020.011510
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Keywords

  • Generalized linear mixed models
  • Ordinal logistic regression with normal random effects
  • Overdispersion
  • Poisson regression with normal random effects

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